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@Article{AmoreKampFrou:2018:GeApMe,
               author = "Amore, Diogo de Jesus and Kampel, Milton and Frouin, Robert",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Scripps Institution 
                         of Oceanography}",
                title = "Geostatistical approach for meteo-oceanographic variables 
                         evaluation at the Brazilian coast",
              journal = "Proceedings of SPIE",
                 year = "2018",
               volume = "10778",
                pages = "107780v",
             keywords = "GWR, geostatistics, sea surface temperature, photosynthetically 
                         active radiation, chlorophyll-a.",
             abstract = "MODIS chlorophyll-a concentration (chla), sea surface temperature 
                         (SST), and photosynthetically active radiation (PAR) were used to 
                         perform a geographically weighted regression (GWR) analysis within 
                         a 150-km buffer of the Brazilian coast. The correlation was 
                         between chla as the regressed variable and SST or PAR as the 
                         predictors. Both a GWR and a Bayesian GWR (BGWR) were used for 
                         evaluating the variables. Colored matrices were plotted for 
                         displaying beta values, significance, residuals, and t-statistics. 
                         Coefficients of determination (R2 ) were computed for all months. 
                         Also, the ratio of the GWR beta estimates and the 95% confidence 
                         interval BGWR estimates was computed. Results showed overall 
                         better R2 for SST than for PAR regression but also better beta 
                         estimates for PAR than for SST in relation to BGWR beta 
                         significance range. Northern regions of the Brazilian coast 
                         exhibited lower statistical significance. July had lowest GWR beta 
                         values and best significance, January highest beta values and 
                         worst significance, and April and October highly variable 
                         results.",
                  doi = "10.1117/12.2500574",
                  url = "http://dx.doi.org/10.1117/12.2500574",
                 issn = "1018-4732",
             language = "en",
           targetfile = "amore_geostatistical.pdf",
        urlaccessdate = "02 dez. 2020"
}


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